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Found 1,571 Skills
When the user wants to model inventory systems with uncertain demand, optimize safety stock levels, implement (s,S) or (Q,r) policies, or analyze service levels under uncertainty. Also use when the user mentions "stochastic inventory," "probabilistic inventory," "(Q,r) policy," "(s,S) policy," "base stock policy," "safety stock optimization," "service level constraints," "lead time demand distribution," "fill rate calculation," or "inventory with demand uncertainty." For deterministic models, see economic-order-quantity or lot-sizing-problems. For single-period uncertainty, see newsvendor-problem.
Migrate GPU/CUDA Triton operators to Triton-Ascend, or rewrite Python/PyTorch operators into Triton-Ascend implementations that can run on Ascend NPU. When clear optimization opportunities are identified, directly output the optimized code, minimal validation script, and troubleshooting instructions. This skill should be prioritized when users mention 昇腾 (Ascend), Ascend, NPU, triton-ascend, Triton operator migration, PyTorch operator rewriting, coreDim, UB overflow, 1D grid, physical core binding, block_ptr, stride, memory access alignment, mask performance, dtype degradation, operator optimization, or directly ask questions like "How to use this skill", "How to run it in the command line", "How to perform migration/validation in a container", even if users do not explicitly say "write a skill" or "perform migration".
This skill provides comprehensive guidance for adapting Wan-series video generation models (Wan2.1/Wan2.2) from NVIDIA CUDA to Huawei Ascend NPU. It should be used when performing NPU migration of DiT-based video diffusion models, including device layer adaptation, operator replacement, distributed parallelism refactoring, attention optimization, VAE parallelization, and model quantization. This skill covers 9 major adaptation domains derived from real-world Wan2.2 CUDA-to-Ascend porting experience.
The Landing Page Audit skill performs comprehensive evaluations of post-click experiences, combining technical performance analysis with conversion rate optimization (CRO) assessment.
The Quality Score Optimization skill provides a systematic framework for diagnosing, tracking, and improving Quality Score across every keyword in a Google Ads account.
V8 JIT Compilation, TurboFan, Maglev, Sparkplug. Load this when needing to understand V8's compilation pipeline, JIT optimization, or JITless mode.
Build type-safe LLM applications with DSPy.rb — Ruby's programmatic prompt framework with signatures, modules, agents, and optimization. Use when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.
Optimize content for AI search engines including Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and Microsoft Copilot. Covers generative engine optimization (GEO), AI citability audits, content structuring for extraction, schema markup, bot access configuration, and monitoring. Use when optimizing for AI search, AI overviews, generative search, LLM visibility, semantic search, entity optimization, or when user mentions AI SEO, GEO, Perplexity citations, ChatGPT visibility, or AI-generated answers.
Interactive installer for Everything Claude Code — guides users through selecting and installing skills and rules to user-level or project-level directories, verifies paths, and optionally optimizes installed files.
Evaluate and score based on the evaluation criteria for vertical short dramas, covering dimensions such as core appealing points and story types. It is suitable for assessing the potential of adapting stories into vertical short dramas and analyzing market competitiveness
When the user wants to solve strip packing problems, pack items into fixed-width strips, or minimize packing height. Also use when the user mentions "strip packing," "cutting stock with fixed width," "ribbon packing," "shelf packing," "minimize height packing," or "2D strip packing problem." For general 2D packing, see 2d-bin-packing. For 3D packing, see 3d-bin-packing.